library(tidyverse)
## ── Attaching packages ────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
## ✔ tibble 1.4.2 ✔ dplyr 0.7.4
## ✔ tidyr 0.8.0 ✔ stringr 1.2.0
## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
3.2.4 Exercises
# 1
ggplot(mpg)
# We see nothing because there are no aesthetics
# 2
nrow(mpg)
## [1] 234
ncol(mpg)
## [1] 11
# 3
# The drive of the car
# 4
ggplot(mpg,aes(y=hwy,x=cyl)) +
geom_point()
# 5
ggplot(mpg,aes(y=class,x=drv)) +
geom_point()
# There are many points in the same positions, so not all data points are visible
3.3.1 Exercises
# 1
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
# Aesthetics take variables as inputs
# In the above code, R creates an equal length vector of strings values (all = "blue")
# ggplot colors the points based on the factor levels of the color vector of strings
# there is only one level ("blue") so all the points have the same color
# We need to manually assign color = "blue" outside of the aesthetic mapping
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
# Note also that the legend no longer appears to indicate the color definitions
# there are no implied meanings to the colors now, the points are simply blue
# 2
# categoricals: manufacturer, model, trans, drv, fl, class
# continuous: displ, year, cyl, cty, hwy
# displ, year, cyl could all be considered categorical as well
mpg
## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr>
## 1 audi a4 1.80 1999 4 auto(l… f 18 29 p
## 2 audi a4 1.80 1999 4 manual… f 21 29 p
## 3 audi a4 2.00 2008 4 manual… f 20 31 p
## 4 audi a4 2.00 2008 4 auto(a… f 21 30 p
## 5 audi a4 2.80 1999 6 auto(l… f 16 26 p
## 6 audi a4 2.80 1999 6 manual… f 18 26 p
## 7 audi a4 3.10 2008 6 auto(a… f 18 27 p
## 8 audi a4 quat… 1.80 1999 4 manual… 4 18 26 p
## 9 audi a4 quat… 1.80 1999 4 auto(l… 4 16 25 p
## 10 audi a4 quat… 2.00 2008 4 manual… 4 20 28 p
## # ... with 224 more rows, and 1 more variable: class <chr>
str(mpg)
## Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
## $ manufacturer: chr "audi" "audi" "audi" "audi" ...
## $ model : chr "a4" "a4" "a4" "a4" ...
## $ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
## $ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
## $ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
## $ trans : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
## $ drv : chr "f" "f" "f" "f" ...
## $ cty : int 18 21 20 21 16 18 18 18 16 20 ...
## $ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
## $ fl : chr "p" "p" "p" "p" ...
## $ class : chr "compact" "compact" "compact" "compact" ...
# 3
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = cty))
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, size = cty))
# ggplot(data = mpg) +
# geom_point(mapping = aes(x = displ, y = hwy, shape = cty))
# When mapping a continuous variable to color and size, gpplot handles them well
# ggplot seems to treat these variables as ordered factors with many levels
# color is not assigned randomly to factor levels, but has a light/dark scale
# Mapping a continuous variable to a shape throws an error
# there is no good way to present continuous data as shapes
# 4
ggplot(data = mpg) +
geom_point(mapping = aes(x = hwy, y = hwy, size = hwy, color = hwy))
# The variables just show up in multiple aesthetics
# 5
?geom_point
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, fill=cty), color = "blue", shape = 21, stroke = 2)
# Stroke sets the width of the line that draws the point geoms
# 6
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = displ < 5))
# R coerces the given info to a vector that fits the plot format
# In this case we get a logical vector
3.5.1 Exercises
# 1
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~cty)
# ggplot converts the continuous to a categorical
# 2
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv~cyl)
ggplot(data = mpg) +
geom_point(mapping = aes(x = drv, y = cyl))
# They mean there are no data points with those combination of categories
# e.g. there are no Rear wheel drive (R) cars with 4 cylinders
# 3
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ .)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ cyl)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~drv, nrow = 3)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ cyl, nrow = 1)
# The . means to facet across all groups on the grid
# The results are effectively the same as facet_wrap, but with cosmetic differences
# 4
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
# Color Plot:
# Easier to compare points from different classes
# Easier to understand which points are outliers, which classes are unusual
# Facet plot:
# Easier to see trends within classes
# As the data points increase, the color plot will become difficult to interpret
# 5
# nrow and ncol determine the number of rows/cols in a facet_wrap plot
# These are implied by the dimensions of the variables given in facet_grid
# 6
# Screens are typically wider than they are tall, so the plot will probably look better
3.6.1 Exercises
# 1
?geom_line
?geom_boxplot
?geom_histogram
?geom_area
# 2
# Scatter plot of hwy ~ displ colored by the drv
# also a line for each drv showing the mean trend, without error bars
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess'
# 3
# show.legend = FALSE suppresses the legend, if you remove it returns to it's default value TRUE
# Hadley suppressed the legend earlier because the graphs were already very small
# 4
# It's a TRUE/FALSE that determines whether the error bars show up
# 5
# They are equivalent
# The data and mapping declared in the initial ggplot statement passes down to the geoms
# 6
ggplot(data = mpg, aes(y = hwy, x = displ)) +
geom_point() +
geom_smooth(se=FALSE)
## `geom_smooth()` using method = 'loess'
# You need to have read the chapter to know drv is the group variable below
ggplot(data = mpg, aes(y = hwy, x = displ, group = drv)) +
geom_point() +
geom_smooth(se=FALSE)
## `geom_smooth()` using method = 'loess'
# Note that you could also apply the group aesthetic to geom_smooth because it makes no difference for geom_point()
ggplot(data = mpg, aes(y = hwy, x = displ, color = drv)) +
geom_point() +
geom_smooth(se=FALSE)
## `geom_smooth()` using method = 'loess'
ggplot(data = mpg, aes(y = hwy, x = displ)) +
geom_point(aes(color = drv)) +
geom_smooth(se=FALSE)
## `geom_smooth()` using method = 'loess'
ggplot(data = mpg, aes(y = hwy, x = displ)) +
geom_point(aes(color = drv)) +
geom_smooth(aes(linetype = drv), se=FALSE)
## `geom_smooth()` using method = 'loess'
ggplot(data = mpg, aes(y = hwy, x = displ)) +
geom_point(aes(fill = drv), shape = 21, color = "white", stroke = 2)
3.7.1 Exercises
# 1
?stat_summary
# geom = "pointrange"
?geom_pointrange
ggplot(data = diamonds) +
geom_pointrange(
mapping = aes(x = cut, y = depth),
stat = "summary",
fun.ymin = min,
fun.ymax = max,
fun.y = median
)
# 2
?geom_col
# I bleieve that geom_col() is equivalent to geom_bar(stat = "identity")
# There might be more to it than that
# 3
# Not really sure of the answer here. The geom-stat pairs always have the same default position?
# 4
?stat_smooth
# This is basically just asking to look at the help
# 5
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, y = ..prop..))
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..))
# In the preceding charts, the proportions are computed across the individual categories
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = color))
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, group = 1, fill = color, y = ..prop..))
# Note that the last chart doesn't work, it is the same as the preceding chart
# When we didn't
3.8.1 Exercises
# 1
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point()
# The points are overlapping, improve by jittering
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_jitter()
# 2
?geom_jitter
# height and width
# 3
?geom_count
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_count()
# geom_count() shows overlapping points as larger points
# this seems generally better than geom_jitter to me
# I'm not sure when geom_jitter is better
# 4
?geom_boxplot
# the default position is dodge
ggplot(mpg, aes(x=drv, y=cty)) +
geom_boxplot()
ggplot(mpg, aes(x=drv, y=cty, group = class)) +
geom_boxplot()
## Warning: position_dodge requires non-overlapping x intervals
# Here we have the boxplots of city mpg grouped by the class variable
# It is trying to make a box for each for each combination of drv+class
# It throws a warning because the boxes overlap horizontally, which is ugly
3.9.1 Exercises
# 1
# stacked bar
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = color))
# I guess this what Mr. Wickham means
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = color)) +
coord_polar()
# 2
?labs
# allows for manual labelling of axes
# 3
?coord_map
?coord_quickmap
# coor_map adjusts plotted latitude/longitude data to account for the spherical shape of the earth
# the space between latitudes is much larger at the equator than at the poles
# coord_quickmap uses an approximation that is much faster but is less accurate, especially near the poles
# 4
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point() +
geom_abline() +
coord_fixed()
# geom_fixed forces a specific ratio of y~x in the graph
# the default ratio=1 doesnt show us much in this graph
# to understand better, set the ratio to 5 or to 0.2
# By default, ggplot choose a coordinate ratio based upon the ranges of x and y
# abline just adds a straight line to the graph
# This is useful for annotations and calling out trends
# No parameters are passed so the abline defaults to y = x
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point() +
geom_abline() +
coord_fixed(ratio = 5)
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point() +
geom_abline() +
coord_fixed(ratio = 0.2)
